Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations2420
Missing cells14554
Missing cells (%)23.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory849.6 B

Variable types

Text2
DateTime1
Categorical14
Numeric7
Unsupported2

Alerts

cant_antecedentes has constant value "1.0" Constant
cant_MontoLimite has constant value "1.0" Constant
Cluster_6 has constant value "5" Constant
Estado is highly overall correlated with cant_noAutenticadoHigh correlation
TipoSocietario is highly overall correlated with cant_noAutenticadoHigh correlation
anio_preinscripcion is highly overall correlated with antiguedad and 1 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_autenticado and 3 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with Estado and 5 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_representante is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 4 other fieldsHigh correlation
dtotal_articulos_provee is highly overall correlated with total_articulos_proveeHigh correlation
monto_total_adjudicado is highly overall correlated with cant_procesos_adjudicado and 2 other fieldsHigh correlation
periodo_preinscripcion is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
total_articulos_provee is highly overall correlated with dtotal_articulos_proveeHigh correlation
Estado is highly imbalanced (60.0%) Imbalance
cant_Apoderado is highly imbalanced (74.2%) Imbalance
cant_representante is highly imbalanced (97.8%) Imbalance
cant_autenticado is highly imbalanced (93.8%) Imbalance
cant_sinMontoLimite is highly imbalanced (92.1%) Imbalance
cant_socios has 50 (2.1%) missing values Missing
cant_apercibimientos has 2420 (100.0%) missing values Missing
cant_suspensiones has 2420 (100.0%) missing values Missing
cant_antecedentes has 2419 (> 99.9%) missing values Missing
cant_Apoderado has 2043 (84.4%) missing values Missing
cant_representante has 357 (14.8%) missing values Missing
cant_noAutenticado has 2397 (99.0%) missing values Missing
cant_MontoLimite has 2418 (99.9%) missing values Missing
CUIT has unique values Unique
Nombre has unique values Unique
cant_apercibimientos is an unsupported type, check if it needs cleaning or further analysis Unsupported
cant_suspensiones is an unsupported type, check if it needs cleaning or further analysis Unsupported
antiguedad has 260 (10.7%) zeros Zeros

Reproduction

Analysis started2025-06-30 18:10:27.711133
Analysis finished2025-06-30 18:10:33.977440
Duration6.27 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct2420
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size179.5 KiB
2025-06-30T15:10:34.111263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length11
Mean length10.971074
Min length9

Characters and Unicode

Total characters26550
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2420 ?
Unique (%)100.0%

Sample

1st row33712286089
2nd row30583184305
3rd row30521417311
4th row30644877805
5th row30708516852
ValueCountFrequency (%)
30623373084 1
 
< 0.1%
30710308051 1
 
< 0.1%
33712286089 1
 
< 0.1%
30583184305 1
 
< 0.1%
30521417311 1
 
< 0.1%
30644877805 1
 
< 0.1%
30708516852 1
 
< 0.1%
30714005789 1
 
< 0.1%
30714749206 1
 
< 0.1%
30708085207 1
 
< 0.1%
Other values (2410) 2410
99.6%
2025-06-30T15:10:34.342251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4512
17.0%
3 4174
15.7%
7 3226
12.2%
1 2857
10.8%
6 2199
8.3%
5 2147
8.1%
9 2032
7.7%
4 1821
6.9%
8 1771
 
6.7%
2 1718
 
6.5%
Other values (22) 93
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4512
17.0%
3 4174
15.7%
7 3226
12.2%
1 2857
10.8%
6 2199
8.3%
5 2147
8.1%
9 2032
7.7%
4 1821
6.9%
8 1771
 
6.7%
2 1718
 
6.5%
Other values (22) 93
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4512
17.0%
3 4174
15.7%
7 3226
12.2%
1 2857
10.8%
6 2199
8.3%
5 2147
8.1%
9 2032
7.7%
4 1821
6.9%
8 1771
 
6.7%
2 1718
 
6.5%
Other values (22) 93
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4512
17.0%
3 4174
15.7%
7 3226
12.2%
1 2857
10.8%
6 2199
8.3%
5 2147
8.1%
9 2032
7.7%
4 1821
6.9%
8 1771
 
6.7%
2 1718
 
6.5%
Other values (22) 93
 
0.4%

Nombre
Text

Unique 

Distinct2420
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size215.4 KiB
2025-06-30T15:10:34.545450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length135
Median length82
Mean length22.117355
Min length3

Characters and Unicode

Total characters53524
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2420 ?
Unique (%)100.0%

Sample

1st rowCOMPAÑÍA DE HIGIENE
2nd rowMATAFUEGOS ORLANDO S.R.L.
3rd rowCONFECCIONES JOSE CONTARTESE Y CIA S.R.L.
4th rowLA BLUSERI S.A.
5th rowCOOPERATIVA DE TRABAJO ARCANGEL LIMITADA
ValueCountFrequency (%)
s.a 629
 
7.7%
srl 585
 
7.1%
de 344
 
4.2%
s.r.l 342
 
4.2%
sa 284
 
3.5%
y 155
 
1.9%
cooperativa 101
 
1.2%
trabajo 94
 
1.1%
argentina 87
 
1.1%
la 70
 
0.9%
Other values (3430) 5528
67.3%
2025-06-30T15:10:34.873202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5799
 
10.8%
A 4338
 
8.1%
S 3826
 
7.1%
R 2919
 
5.5%
E 2607
 
4.9%
. 2570
 
4.8%
I 2551
 
4.8%
O 2295
 
4.3%
L 2243
 
4.2%
N 1738
 
3.2%
Other values (84) 22638
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5799
 
10.8%
A 4338
 
8.1%
S 3826
 
7.1%
R 2919
 
5.5%
E 2607
 
4.9%
. 2570
 
4.8%
I 2551
 
4.8%
O 2295
 
4.3%
L 2243
 
4.2%
N 1738
 
3.2%
Other values (84) 22638
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5799
 
10.8%
A 4338
 
8.1%
S 3826
 
7.1%
R 2919
 
5.5%
E 2607
 
4.9%
. 2570
 
4.8%
I 2551
 
4.8%
O 2295
 
4.3%
L 2243
 
4.2%
N 1738
 
3.2%
Other values (84) 22638
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5799
 
10.8%
A 4338
 
8.1%
S 3826
 
7.1%
R 2919
 
5.5%
E 2607
 
4.9%
. 2570
 
4.8%
I 2551
 
4.8%
O 2295
 
4.3%
L 2243
 
4.2%
N 1738
 
3.2%
Other values (84) 22638
42.3%
Distinct1018
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
Minimum2016-01-09 00:00:00
Maximum2022-12-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-30T15:10:34.977891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:35.082713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size183.5 KiB
Inscripto
1898 
Desactualizado doc. vencidos
243 
Pre Inscripto
196 
Desactualizado mantención
 
44
Desactualizado Por Clase
 
24
Other values (2)
 
15

Length

Max length28
Median length9
Mean length11.719421
Min length9

Characters and Unicode

Total characters28361
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInscripto
2nd rowDesactualizado doc. vencidos
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 1898
78.4%
Desactualizado doc. vencidos 243
 
10.0%
Pre Inscripto 196
 
8.1%
Desactualizado mantención 44
 
1.8%
Desactualizado Por Clase 24
 
1.0%
En Evaluacion 8
 
0.3%
Con Solicitud De Baja 7
 
0.3%

Length

2025-06-30T15:10:35.620068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:35.705496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 2094
65.0%
desactualizado 311
 
9.6%
doc 243
 
7.5%
vencidos 243
 
7.5%
pre 196
 
6.1%
mantención 44
 
1.4%
por 24
 
0.7%
clase 24
 
0.7%
en 8
 
0.2%
evaluacion 8
 
0.2%
Other values (4) 28
 
0.9%

Most occurring characters

ValueCountFrequency (%)
c 2950
10.4%
o 2937
10.4%
i 2714
9.6%
s 2672
9.4%
n 2492
8.8%
t 2456
8.7%
r 2314
8.2%
I 2094
7.4%
p 2094
7.4%
a 1031
 
3.6%
Other values (17) 4607
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 2950
10.4%
o 2937
10.4%
i 2714
9.6%
s 2672
9.4%
n 2492
8.8%
t 2456
8.7%
r 2314
8.2%
I 2094
7.4%
p 2094
7.4%
a 1031
 
3.6%
Other values (17) 4607
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 2950
10.4%
o 2937
10.4%
i 2714
9.6%
s 2672
9.4%
n 2492
8.8%
t 2456
8.7%
r 2314
8.2%
I 2094
7.4%
p 2094
7.4%
a 1031
 
3.6%
Other values (17) 4607
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 2950
10.4%
o 2937
10.4%
i 2714
9.6%
s 2672
9.4%
n 2492
8.8%
t 2456
8.7%
r 2314
8.2%
I 2094
7.4%
p 2094
7.4%
a 1031
 
3.6%
Other values (17) 4607
16.2%

TipoSocietario
Categorical

High correlation 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size224.7 KiB
Sociedad Anónima
999 
S.R.L
962 
Otras Formas Societarias
146 
Cooperativas
111 
PJ Extranjero Sin Sucursal
103 
Other values (4)
 
99

Length

Max length29
Median length26
Mean length12.471074
Min length5

Characters and Unicode

Total characters30180
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS.R.L
2nd rowS.R.L
3rd rowS.R.L
4th rowSociedad Anónima
5th rowCooperativas

Common Values

ValueCountFrequency (%)
Sociedad Anónima 999
41.3%
S.R.L 962
39.8%
Otras Formas Societarias 146
 
6.0%
Cooperativas 111
 
4.6%
PJ Extranjero Sin Sucursal 103
 
4.3%
Organismo Publico 47
 
1.9%
Sociedades De Hecho 39
 
1.6%
Unión Transitoria de Empresas 10
 
0.4%
Persona Física 3
 
0.1%

Length

2025-06-30T15:10:35.844998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:35.927605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sociedad 999
23.9%
anónima 999
23.9%
s.r.l 962
23.0%
otras 146
 
3.5%
formas 146
 
3.5%
societarias 146
 
3.5%
cooperativas 111
 
2.7%
pj 103
 
2.5%
extranjero 103
 
2.5%
sin 103
 
2.5%
Other values (11) 360
 
8.6%

Most occurring characters

ValueCountFrequency (%)
a 3132
 
10.4%
i 2670
 
8.8%
S 2352
 
7.8%
n 2284
 
7.6%
d 2086
 
6.9%
. 1924
 
6.4%
o 1801
 
6.0%
1758
 
5.8%
e 1538
 
5.1%
c 1376
 
4.6%
Other values (28) 9259
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3132
 
10.4%
i 2670
 
8.8%
S 2352
 
7.8%
n 2284
 
7.6%
d 2086
 
6.9%
. 1924
 
6.4%
o 1801
 
6.0%
1758
 
5.8%
e 1538
 
5.1%
c 1376
 
4.6%
Other values (28) 9259
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3132
 
10.4%
i 2670
 
8.8%
S 2352
 
7.8%
n 2284
 
7.6%
d 2086
 
6.9%
. 1924
 
6.4%
o 1801
 
6.0%
1758
 
5.8%
e 1538
 
5.1%
c 1376
 
4.6%
Other values (28) 9259
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3132
 
10.4%
i 2670
 
8.8%
S 2352
 
7.8%
n 2284
 
7.6%
d 2086
 
6.9%
. 1924
 
6.4%
o 1801
 
6.0%
1758
 
5.8%
e 1538
 
5.1%
c 1376
 
4.6%
Other values (28) 9259
30.7%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201792.43
Minimum201607
Maximum202211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2025-06-30T15:10:36.042899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201607
5-th percentile201610
Q1201702
median201709
Q3201904
95-th percentile202110
Maximum202211
Range604
Interquartile range (IQR)202

Descriptive statistics

Standard deviation162.63707
Coefficient of variation (CV)0.00080596219
Kurtosis-0.0034933334
Mean201792.43
Median Absolute Deviation (MAD)97
Skewness0.97973166
Sum4.8833769 × 108
Variance26450.817
MonotonicityNot monotonic
2025-06-30T15:10:36.171545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201611 199
 
8.2%
201701 124
 
5.1%
201703 101
 
4.2%
201612 97
 
4.0%
201706 94
 
3.9%
201708 87
 
3.6%
201704 83
 
3.4%
201702 82
 
3.4%
201705 79
 
3.3%
201707 78
 
3.2%
Other values (67) 1396
57.7%
ValueCountFrequency (%)
201607 4
 
0.2%
201608 32
 
1.3%
201609 31
 
1.3%
201610 69
 
2.9%
201611 199
8.2%
201612 97
4.0%
201701 124
5.1%
201702 82
3.4%
201703 101
4.2%
201704 83
3.4%
ValueCountFrequency (%)
202211 3
 
0.1%
202210 3
 
0.1%
202209 9
0.4%
202208 7
0.3%
202207 9
0.4%
202206 7
0.3%
202205 10
0.4%
202204 10
0.4%
202203 10
0.4%
202202 9
0.4%

anio_preinscripcion
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.857
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2025-06-30T15:10:36.260917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2017
Q32019
95-th percentile2021
Maximum2022
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6329759
Coefficient of variation (CV)0.00080926246
Kurtosis-0.015877
Mean2017.857
Median Absolute Deviation (MAD)1
Skewness0.95810061
Sum4883214
Variance2.6666104
MonotonicityNot monotonic
2025-06-30T15:10:36.328619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2017 925
38.2%
2016 432
17.9%
2018 398
16.4%
2019 229
 
9.5%
2021 178
 
7.4%
2020 176
 
7.3%
2022 82
 
3.4%
ValueCountFrequency (%)
2016 432
17.9%
2017 925
38.2%
2018 398
16.4%
2019 229
 
9.5%
2020 176
 
7.3%
2021 178
 
7.4%
2022 82
 
3.4%
ValueCountFrequency (%)
2022 82
 
3.4%
2021 178
 
7.4%
2020 176
 
7.3%
2019 229
 
9.5%
2018 398
16.4%
2017 925
38.2%
2016 432
17.9%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)3.2%
Missing10
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean6.459751
Minimum1
Maximum149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2025-06-30T15:10:36.428979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile27
Maximum149
Range148
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.163385
Coefficient of variation (CV)1.8829496
Kurtosis31.528492
Mean6.459751
Median Absolute Deviation (MAD)1
Skewness4.7761773
Sum15568
Variance147.94794
MonotonicityNot monotonic
2025-06-30T15:10:36.542782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 873
36.1%
2 429
17.7%
3 228
 
9.4%
4 153
 
6.3%
5 94
 
3.9%
6 88
 
3.6%
7 52
 
2.1%
9 51
 
2.1%
8 42
 
1.7%
10 38
 
1.6%
Other values (67) 362
15.0%
ValueCountFrequency (%)
1 873
36.1%
2 429
17.7%
3 228
 
9.4%
4 153
 
6.3%
5 94
 
3.9%
6 88
 
3.6%
7 52
 
2.1%
8 42
 
1.7%
9 51
 
2.1%
10 38
 
1.6%
ValueCountFrequency (%)
149 1
 
< 0.1%
135 1
 
< 0.1%
122 1
 
< 0.1%
119 1
 
< 0.1%
99 1
 
< 0.1%
98 1
 
< 0.1%
87 1
 
< 0.1%
86 3
0.1%
81 1
 
< 0.1%
80 1
 
< 0.1%

monto_total_adjudicado
Real number (ℝ)

High correlation 

Distinct2379
Distinct (%)98.7%
Missing10
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1.1475416 × 108
Minimum0
Maximum7.6758617 × 109
Zeros24
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2025-06-30T15:10:36.661957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29205.3
Q1804735.36
median4947927.3
Q328387727
95-th percentile3.3052073 × 108
Maximum7.6758617 × 109
Range7.6758617 × 109
Interquartile range (IQR)27582992

Descriptive statistics

Standard deviation5.2634231 × 108
Coefficient of variation (CV)4.586695
Kurtosis72.411374
Mean1.1475416 × 108
Median Absolute Deviation (MAD)4837142.3
Skewness7.7658383
Sum2.7655751 × 1011
Variance2.7703622 × 1017
MonotonicityNot monotonic
2025-06-30T15:10:36.778583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
1.0%
58802999.64 2
 
0.1%
612000 2
 
0.1%
637500 2
 
0.1%
30600000 2
 
0.1%
534820 2
 
0.1%
40000 2
 
0.1%
33686842.11 2
 
0.1%
254100 2
 
0.1%
59014285.71 1
 
< 0.1%
Other values (2369) 2369
97.9%
(Missing) 10
 
0.4%
ValueCountFrequency (%)
0 24
1.0%
0.023181818 1
 
< 0.1%
4.25 1
 
< 0.1%
9.471428571 1
 
< 0.1%
30.6 1
 
< 0.1%
59.49779221 1
 
< 0.1%
70.08857143 1
 
< 0.1%
102 1
 
< 0.1%
143.65 1
 
< 0.1%
680 1
 
< 0.1%
ValueCountFrequency (%)
7675861678 1
< 0.1%
6782343325 1
< 0.1%
6458785714 1
< 0.1%
6020777934 1
< 0.1%
5323342711 1
< 0.1%
5137870354 1
< 0.1%
5092092510 1
< 0.1%
4848542284 1
< 0.1%
4746364182 1
< 0.1%
4463290979 1
< 0.1%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1768595
Minimum0
Maximum5
Zeros260
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2025-06-30T15:10:36.874247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5551464
Coefficient of variation (CV)0.4895232
Kurtosis-0.44792199
Mean3.1768595
Median Absolute Deviation (MAD)1
Skewness-0.81330884
Sum7688
Variance2.4184804
MonotonicityNot monotonic
2025-06-30T15:10:36.936748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 925
38.2%
5 432
17.9%
3 398
16.4%
0 260
 
10.7%
2 229
 
9.5%
1 176
 
7.3%
ValueCountFrequency (%)
0 260
 
10.7%
1 176
 
7.3%
2 229
 
9.5%
3 398
16.4%
4 925
38.2%
5 432
17.9%
ValueCountFrequency (%)
5 432
17.9%
4 925
38.2%
3 398
16.4%
2 229
 
9.5%
1 176
 
7.3%
0 260
 
10.7%

provincia
Categorical

Distinct27
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size175.3 KiB
CABA
1217 
Buenos Aires
520 
Córdoba
132 
Santa Fe
 
107
Extranjera
 
103
Other values (22)
341 

Length

Max length19
Median length4
Mean length6.8987603
Min length1

Characters and Unicode

Total characters16695
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBuenos Aires
2nd rowCABA
3rd rowCABA
4th rowCABA
5th rowBuenos Aires

Common Values

ValueCountFrequency (%)
CABA 1217
50.3%
Buenos Aires 520
21.5%
Córdoba 132
 
5.5%
Santa Fe 107
 
4.4%
Extranjera 103
 
4.3%
Mendoza 47
 
1.9%
Chubut 33
 
1.4%
Misiones 28
 
1.2%
Entre Rios 23
 
1.0%
Rio Negro 21
 
0.9%
Other values (17) 189
 
7.8%

Length

2025-06-30T15:10:37.026224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caba 1217
38.2%
buenos 520
16.3%
aires 520
16.3%
córdoba 132
 
4.1%
santa 115
 
3.6%
fe 107
 
3.4%
extranjera 103
 
3.2%
mendoza 47
 
1.5%
chubut 33
 
1.0%
misiones 28
 
0.9%
Other values (27) 365
 
11.5%

Most occurring characters

ValueCountFrequency (%)
A 2954
17.7%
B 1737
10.4%
e 1475
8.8%
C 1434
8.6%
s 1175
 
7.0%
r 998
 
6.0%
n 946
 
5.7%
o 888
 
5.3%
a 834
 
5.0%
767
 
4.6%
Other values (30) 3487
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2954
17.7%
B 1737
10.4%
e 1475
8.8%
C 1434
8.6%
s 1175
 
7.0%
r 998
 
6.0%
n 946
 
5.7%
o 888
 
5.3%
a 834
 
5.0%
767
 
4.6%
Other values (30) 3487
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2954
17.7%
B 1737
10.4%
e 1475
8.8%
C 1434
8.6%
s 1175
 
7.0%
r 998
 
6.0%
n 946
 
5.7%
o 888
 
5.3%
a 834
 
5.0%
767
 
4.6%
Other values (30) 3487
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2954
17.7%
B 1737
10.4%
e 1475
8.8%
C 1434
8.6%
s 1175
 
7.0%
r 998
 
6.0%
n 946
 
5.7%
o 888
 
5.3%
a 834
 
5.0%
767
 
4.6%
Other values (30) 3487
20.9%

cant_socios
Real number (ℝ)

Missing 

Distinct20
Distinct (%)0.8%
Missing50
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean2.335865
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2025-06-30T15:10:37.110097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum31
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8779397
Coefficient of variation (CV)0.803959
Kurtosis44.910037
Mean2.335865
Median Absolute Deviation (MAD)1
Skewness4.9234925
Sum5536
Variance3.5266574
MonotonicityNot monotonic
2025-06-30T15:10:37.195955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 819
33.8%
1 806
33.3%
3 416
17.2%
4 164
 
6.8%
5 74
 
3.1%
6 30
 
1.2%
7 16
 
0.7%
8 11
 
0.5%
9 8
 
0.3%
10 7
 
0.3%
Other values (10) 19
 
0.8%
(Missing) 50
 
2.1%
ValueCountFrequency (%)
1 806
33.3%
2 819
33.8%
3 416
17.2%
4 164
 
6.8%
5 74
 
3.1%
6 30
 
1.2%
7 16
 
0.7%
8 11
 
0.5%
9 8
 
0.3%
10 7
 
0.3%
ValueCountFrequency (%)
31 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
17 3
0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 4
0.2%
13 1
 
< 0.1%
12 3
0.1%
11 3
0.1%

cant_apercibimientos
Unsupported

Missing  Rejected  Unsupported 

Missing2420
Missing (%)100.0%
Memory size37.8 KiB

cant_suspensiones
Unsupported

Missing  Rejected  Unsupported 

Missing2420
Missing (%)100.0%
Memory size37.8 KiB

cant_antecedentes
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing2419
Missing (%)> 99.9%
Memory size151.3 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 2419
> 99.9%

Length

2025-06-30T15:10:37.278802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:37.327769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

cant_Apoderado
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.8%
Missing2043
Missing (%)84.4%
Memory size152.7 KiB
1.0
349 
2.0
 
26
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1131
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 349
 
14.4%
2.0 26
 
1.1%
3.0 2
 
0.1%
(Missing) 2043
84.4%

Length

2025-06-30T15:10:37.395116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:37.460868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 349
92.6%
2.0 26
 
6.9%
3.0 2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 377
33.3%
0 377
33.3%
1 349
30.9%
2 26
 
2.3%
3 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 377
33.3%
0 377
33.3%
1 349
30.9%
2 26
 
2.3%
3 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 377
33.3%
0 377
33.3%
1 349
30.9%
2 26
 
2.3%
3 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 377
33.3%
0 377
33.3%
1 349
30.9%
2 26
 
2.3%
3 2
 
0.2%

cant_representante
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.1%
Missing357
Missing (%)14.8%
Memory size159.3 KiB
1.0
2056 
2.0
 
6
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6189
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2056
85.0%
2.0 6
 
0.2%
4.0 1
 
< 0.1%
(Missing) 357
 
14.8%

Length

2025-06-30T15:10:37.508875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:37.555753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2056
99.7%
2.0 6
 
0.3%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 2063
33.3%
0 2063
33.3%
1 2056
33.2%
2 6
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2063
33.3%
0 2063
33.3%
1 2056
33.2%
2 6
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2063
33.3%
0 2063
33.3%
1 2056
33.2%
2 6
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2063
33.3%
0 2063
33.3%
1 2056
33.2%
2 6
 
0.1%
4 1
 
< 0.1%

cant_autenticado
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size160.7 KiB
1.0
2390 
2.0
 
29
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7260
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2390
98.8%
2.0 29
 
1.2%
3.0 1
 
< 0.1%

Length

2025-06-30T15:10:37.628853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:37.678824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2390
98.8%
2.0 29
 
1.2%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 2420
33.3%
0 2420
33.3%
1 2390
32.9%
2 29
 
0.4%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2420
33.3%
0 2420
33.3%
1 2390
32.9%
2 29
 
0.4%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2420
33.3%
0 2420
33.3%
1 2390
32.9%
2 29
 
0.4%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2420
33.3%
0 2420
33.3%
1 2390
32.9%
2 29
 
0.4%
3 1
 
< 0.1%

cant_noAutenticado
Categorical

High correlation  Missing 

Distinct3
Distinct (%)13.0%
Missing2397
Missing (%)99.0%
Memory size151.3 KiB
1.0
19 
2.0
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 19
 
0.8%
2.0 3
 
0.1%
3.0 1
 
< 0.1%
(Missing) 2397
99.0%

Length

2025-06-30T15:10:37.749269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:37.812170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 19
82.6%
2.0 3
 
13.0%
3.0 1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
. 23
33.3%
0 23
33.3%
1 19
27.5%
2 3
 
4.3%
3 1
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 23
33.3%
0 23
33.3%
1 19
27.5%
2 3
 
4.3%
3 1
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 23
33.3%
0 23
33.3%
1 19
27.5%
2 3
 
4.3%
3 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 23
33.3%
0 23
33.3%
1 19
27.5%
2 3
 
4.3%
3 1
 
1.4%

cant_sinMontoLimite
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size160.7 KiB
1.0
2369 
2.0
 
46
3.0
 
4
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7260
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2369
97.9%
2.0 46
 
1.9%
3.0 4
 
0.2%
4.0 1
 
< 0.1%

Length

2025-06-30T15:10:37.875404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:37.922268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2369
97.9%
2.0 46
 
1.9%
3.0 4
 
0.2%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 2420
33.3%
0 2420
33.3%
1 2369
32.6%
2 46
 
0.6%
3 4
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2420
33.3%
0 2420
33.3%
1 2369
32.6%
2 46
 
0.6%
3 4
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2420
33.3%
0 2420
33.3%
1 2369
32.6%
2 46
 
0.6%
3 4
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2420
33.3%
0 2420
33.3%
1 2369
32.6%
2 46
 
0.6%
3 4
 
0.1%
4 1
 
< 0.1%

cant_MontoLimite
Categorical

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing2418
Missing (%)99.9%
Memory size151.3 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
0.1%
(Missing) 2418
99.9%

Length

2025-06-30T15:10:37.991762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:38.038633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct237
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.903306
Minimum1
Maximum831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2025-06-30T15:10:38.102000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q334
95-th percentile161
Maximum831
Range830
Interquartile range (IQR)32

Descriptive statistics

Standard deviation66.572024
Coefficient of variation (CV)1.963585
Kurtosis23.764017
Mean33.903306
Median Absolute Deviation (MAD)7
Skewness4.0958506
Sum82046
Variance4431.8344
MonotonicityNot monotonic
2025-06-30T15:10:38.212416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 500
20.7%
2 198
 
8.2%
3 133
 
5.5%
4 112
 
4.6%
5 83
 
3.4%
6 76
 
3.1%
8 70
 
2.9%
7 54
 
2.2%
10 49
 
2.0%
12 41
 
1.7%
Other values (227) 1104
45.6%
ValueCountFrequency (%)
1 500
20.7%
2 198
 
8.2%
3 133
 
5.5%
4 112
 
4.6%
5 83
 
3.4%
6 76
 
3.1%
7 54
 
2.2%
8 70
 
2.9%
9 36
 
1.5%
10 49
 
2.0%
ValueCountFrequency (%)
831 1
< 0.1%
580 1
< 0.1%
560 1
< 0.1%
532 1
< 0.1%
525 1
< 0.1%
520 1
< 0.1%
514 1
< 0.1%
496 1
< 0.1%
445 1
< 0.1%
438 1
< 0.1%
Distinct21
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size204.6 KiB
(222.964.579- 46.172.150.151]
 
163
(30.451.916- 46.718.747]
 
156
(19.975.532- 30.451.916]
 
152
(13.557.176- 19.975.532]
 
136
(89.439.449- 222.964.579]
 
130
Other values (16)
1683 

Length

Max length29
Median length25
Mean length21.595041
Min length3

Characters and Unicode

Total characters52260
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(4.727.330- 6.702.697]
2nd row(4.727.330- 6.702.697]
3rd row(222.964.579- 46.172.150.151]
4th row(3.396.600- 4.727.330]
5th row(19.975.532- 30.451.916]

Common Values

ValueCountFrequency (%)
(222.964.579- 46.172.150.151] 163
 
6.7%
(30.451.916- 46.718.747] 156
 
6.4%
(19.975.532- 30.451.916] 152
 
6.3%
(13.557.176- 19.975.532] 136
 
5.6%
(89.439.449- 222.964.579] 130
 
5.4%
(-0- 33.011] 127
 
5.2%
(46.718.747- 89.439.449] 127
 
5.2%
(3.396.600- 4.727.330] 127
 
5.2%
(9.424.898- 13.557.176] 125
 
5.2%
(4.727.330- 6.702.697] 121
 
5.0%
Other values (11) 1056
43.6%

Length

2025-06-30T15:10:38.328712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30.451.916 308
 
6.4%
222.964.579 293
 
6.1%
19.975.532 288
 
6.0%
46.718.747 283
 
5.9%
13.557.176 261
 
5.4%
89.439.449 257
 
5.3%
4.727.330 248
 
5.1%
3.396.600 241
 
5.0%
9.424.898 238
 
4.9%
6.702.697 234
 
4.8%
Other values (12) 2179
45.1%

Most occurring characters

ValueCountFrequency (%)
. 8310
15.9%
7 4903
9.4%
9 4439
 
8.5%
3 3776
 
7.2%
4 3444
 
6.6%
1 3414
 
6.5%
6 3084
 
5.9%
2 3082
 
5.9%
0 3034
 
5.8%
5 2883
 
5.5%
Other values (7) 11891
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 8310
15.9%
7 4903
9.4%
9 4439
 
8.5%
3 3776
 
7.2%
4 3444
 
6.6%
1 3414
 
6.5%
6 3084
 
5.9%
2 3082
 
5.9%
0 3034
 
5.8%
5 2883
 
5.5%
Other values (7) 11891
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 8310
15.9%
7 4903
9.4%
9 4439
 
8.5%
3 3776
 
7.2%
4 3444
 
6.6%
1 3414
 
6.5%
6 3084
 
5.9%
2 3082
 
5.9%
0 3034
 
5.8%
5 2883
 
5.5%
Other values (7) 11891
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 8310
15.9%
7 4903
9.4%
9 4439
 
8.5%
3 3776
 
7.2%
4 3444
 
6.6%
1 3414
 
6.5%
6 3084
 
5.9%
2 3082
 
5.9%
0 3034
 
5.8%
5 2883
 
5.5%
Other values (7) 11891
22.8%
Distinct10
Distinct (%)0.4%
Missing10
Missing (%)0.4%
Memory size180.5 KiB
(0.999, 2.0]
1302 
(2.0, 3.0]
228 
(3.0, 4.0]
153 
(8.0, 12.0]
147 
(19.0, 39.0]
 
124
Other values (5)
456 

Length

Max length14
Median length12
Mean length11.445228
Min length10

Characters and Unicode

Total characters27583
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(0.999, 2.0]
2nd row(6.0, 8.0]
3rd row(19.0, 39.0]
4th row(4.0, 5.0]
5th row(5.0, 6.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 1302
53.8%
(2.0, 3.0] 228
 
9.4%
(3.0, 4.0] 153
 
6.3%
(8.0, 12.0] 147
 
6.1%
(19.0, 39.0] 124
 
5.1%
(12.0, 19.0] 118
 
4.9%
(6.0, 8.0] 94
 
3.9%
(4.0, 5.0] 94
 
3.9%
(5.0, 6.0] 88
 
3.6%
(39.0, 1214.0] 62
 
2.6%
(Missing) 10
 
0.4%

Length

2025-06-30T15:10:38.407485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:38.493909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1530
31.7%
0.999 1302
27.0%
3.0 381
 
7.9%
12.0 265
 
5.5%
4.0 247
 
5.1%
19.0 242
 
5.0%
8.0 241
 
5.0%
39.0 186
 
3.9%
6.0 182
 
3.8%
5.0 182
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 4820
17.5%
. 4820
17.5%
9 4334
15.7%
( 2410
8.7%
, 2410
8.7%
2410
8.7%
] 2410
8.7%
2 1857
 
6.7%
1 631
 
2.3%
3 567
 
2.1%
Other values (4) 914
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4820
17.5%
. 4820
17.5%
9 4334
15.7%
( 2410
8.7%
, 2410
8.7%
2410
8.7%
] 2410
8.7%
2 1857
 
6.7%
1 631
 
2.3%
3 567
 
2.1%
Other values (4) 914
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4820
17.5%
. 4820
17.5%
9 4334
15.7%
( 2410
8.7%
, 2410
8.7%
2410
8.7%
] 2410
8.7%
2 1857
 
6.7%
1 631
 
2.3%
3 567
 
2.1%
Other values (4) 914
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4820
17.5%
. 4820
17.5%
9 4334
15.7%
( 2410
8.7%
, 2410
8.7%
2410
8.7%
] 2410
8.7%
2 1857
 
6.7%
1 631
 
2.3%
3 567
 
2.1%
Other values (4) 914
 
3.3%

dtotal_articulos_provee
Categorical

High correlation 

Distinct15
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size181.2 KiB
(0.999, 2.0]
698 
(4.0, 6.0]
159 
(15.0, 21.0]
155 
(40.0, 58.0]
151 
(58.0, 97.6]
141 
Other values (10)
1116 

Length

Max length15
Median length12
Mean length11.666529
Min length10

Characters and Unicode

Total characters28233
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(29.0, 40.0]
2nd row(21.0, 29.0]
3rd row(40.0, 58.0]
4th row(40.0, 58.0]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 698
28.8%
(4.0, 6.0] 159
 
6.6%
(15.0, 21.0] 155
 
6.4%
(40.0, 58.0] 151
 
6.2%
(58.0, 97.6] 141
 
5.8%
(2.0, 3.0] 133
 
5.5%
(29.0, 40.0] 133
 
5.5%
(21.0, 29.0] 132
 
5.5%
(11.0, 15.0] 129
 
5.3%
(6.0, 8.0] 124
 
5.1%
Other values (5) 465
19.2%

Length

2025-06-30T15:10:38.603258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 831
17.2%
0.999 698
14.4%
58.0 292
 
6.0%
21.0 287
 
5.9%
15.0 284
 
5.9%
40.0 284
 
5.9%
6.0 283
 
5.8%
4.0 271
 
5.6%
29.0 265
 
5.5%
97.6 255
 
5.3%
Other values (6) 1090
22.5%

Most occurring characters

ValueCountFrequency (%)
0 4869
17.2%
. 4840
17.1%
9 2654
9.4%
( 2420
8.6%
, 2420
8.6%
2420
8.6%
] 2420
8.6%
1 1495
 
5.3%
2 1383
 
4.9%
6 770
 
2.7%
Other values (5) 2542
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4869
17.2%
. 4840
17.1%
9 2654
9.4%
( 2420
8.6%
, 2420
8.6%
2420
8.6%
] 2420
8.6%
1 1495
 
5.3%
2 1383
 
4.9%
6 770
 
2.7%
Other values (5) 2542
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4869
17.2%
. 4840
17.1%
9 2654
9.4%
( 2420
8.6%
, 2420
8.6%
2420
8.6%
] 2420
8.6%
1 1495
 
5.3%
2 1383
 
4.9%
6 770
 
2.7%
Other values (5) 2542
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4869
17.2%
. 4840
17.1%
9 2654
9.4%
( 2420
8.6%
, 2420
8.6%
2420
8.6%
] 2420
8.6%
1 1495
 
5.3%
2 1383
 
4.9%
6 770
 
2.7%
Other values (5) 2542
9.0%

Cluster_6
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.0 KiB
5
2420 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2420
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 2420
100.0%

Length

2025-06-30T15:10:38.681382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:38.728162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 2420
100.0%

Most occurring characters

ValueCountFrequency (%)
5 2420
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 2420
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 2420
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 2420
100.0%

Interactions

2025-06-30T15:10:32.761745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.010518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.703830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.227061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.860512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.502059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.113336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.852138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.112448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.776217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.327019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.963010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.594205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.212989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.929402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.213010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.835856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.413002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.027206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.663003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.297069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:33.010977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.313202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.912844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.500793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.125939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.749278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.396909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:33.109896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.409518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.995071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.596513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.229347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.843924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.495212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:33.195527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.510716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.079491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.677421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.309592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.929782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.579623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:33.279634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:29.593225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.160082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:30.775131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:31.410393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.011425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:32.680012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-30T15:10:38.775112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_sociosdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.2980.1460.1590.0000.0001.0000.0260.0310.0000.0000.1120.1450.1180.0000.1450.2770.040
TipoSocietario0.2981.0000.1640.1750.2630.1040.5420.0480.2210.1910.0990.0600.1710.0820.1080.1590.3730.012
anio_preinscripcion0.1460.1641.000-1.0000.0000.0000.000-0.3330.0000.024-0.1660.1420.1010.070-0.2180.9660.152-0.084
antiguedad0.1590.175-1.0001.0000.0480.0150.0000.3320.0000.0320.1670.1580.1090.0800.217-0.9660.1530.084
cant_Apoderado0.0000.2630.0000.0481.0000.6730.5480.3801.0000.6950.3200.2740.0000.0450.2780.0000.0000.183
cant_autenticado0.0000.1040.0000.0150.6731.0001.0000.1960.4680.6610.2720.1140.1810.0000.3810.0000.0000.129
cant_noAutenticado1.0000.5420.0000.0000.5481.0001.0000.0000.6580.9750.0000.0000.0000.0000.2040.0000.0000.000
cant_procesos_adjudicado0.0260.048-0.3330.3320.3800.1960.0001.0000.0000.1600.0420.4320.1360.0860.541-0.3480.0000.287
cant_representante0.0310.2210.0000.0001.0000.4680.6580.0001.0000.7890.4340.0000.0540.0290.5340.0000.0000.000
cant_sinMontoLimite0.0000.1910.0240.0320.6950.6610.9750.1600.7891.0000.3970.0970.1800.0000.5340.0140.0000.073
cant_socios0.0000.099-0.1660.1670.3200.2720.0000.0420.4340.3971.0000.0350.0000.0000.117-0.1650.000-0.025
dcant_procesos_adjudicado0.1120.0600.1420.1580.2740.1140.0000.4320.0000.0970.0351.0000.2030.0990.1240.1300.0120.071
dmonto_total_adjudicado0.1450.1710.1010.1090.0000.1810.0000.1360.0540.1800.0000.2031.0000.0410.2120.0930.1110.036
dtotal_articulos_provee0.1180.0820.0700.0800.0450.0000.0000.0860.0290.0000.0000.0990.0411.0000.0270.0670.0370.599
monto_total_adjudicado0.0000.108-0.2180.2170.2780.3810.2040.5410.5340.5340.1170.1240.2120.0271.000-0.2290.0000.108
periodo_preinscripcion0.1450.1590.966-0.9660.0000.0000.000-0.3480.0000.014-0.1650.1300.0930.067-0.2291.0000.139-0.107
provincia0.2770.3730.1520.1530.0000.0000.0000.0000.0000.0000.0000.0120.1110.0370.0000.1391.0000.000
total_articulos_provee0.0400.012-0.0840.0840.1830.1290.0000.2870.0000.073-0.0250.0710.0360.5990.108-0.1070.0001.000

Missing values

2025-06-30T15:10:33.443552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-30T15:10:33.661722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-30T15:10:33.858597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
433712286089COMPAÑÍA DE HIGIENE26/10/2016InscriptoS.R.L20161020161.05.434774e+065.0Buenos Aires2.0NaNNaNNaNNaN1.01.0NaN1.0NaN38.0(4.727.330- 6.702.697](0.999, 2.0](29.0, 40.0]5
530583184305MATAFUEGOS ORLANDO S.R.L.02/11/2016Desactualizado doc. vencidosS.R.L20161120167.05.655964e+065.0CABA3.0NaNNaNNaNNaN1.01.0NaN1.0NaN22.0(4.727.330- 6.702.697](6.0, 8.0](21.0, 29.0]5
730521417311CONFECCIONES JOSE CONTARTESE Y CIA S.R.L.12/09/2016InscriptoS.R.L201609201637.01.823406e+095.0CABA2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN50.0(222.964.579- 46.172.150.151](19.0, 39.0](40.0, 58.0]5
1030644877805LA BLUSERI S.A.12/09/2016InscriptoSociedad Anónima20160920165.04.224898e+065.0CABA3.0NaNNaNNaNNaN1.01.0NaN1.0NaN51.0(3.396.600- 4.727.330](4.0, 5.0](40.0, 58.0]5
1230708516852COOPERATIVA DE TRABAJO ARCANGEL LIMITADA19/10/2016InscriptoCooperativas20161020166.02.781002e+075.0Buenos Aires4.0NaNNaNNaN1.0NaN1.0NaN1.0NaN2.0(19.975.532- 30.451.916](5.0, 6.0](0.999, 2.0]5
1830714005789SIMA POWER SYSTEMS SRL16/08/2016Desactualizado Por ClaseS.R.L20160820161.09.405250e+055.0CABA1.0NaNNaNNaNNaN1.01.0NaN1.0NaN8.0(890.758- 1.302.657](0.999, 2.0](6.0, 8.0]5
2730714749206Ebox S.A.29/09/2016InscriptoSociedad Anónima201609201622.01.224767e+085.0CABA2.0NaNNaNNaNNaN1.01.0NaN1.0NaN124.0(89.439.449- 222.964.579](19.0, 39.0](97.6, 161.0]5
3330708085207TNGROUP S.A.16/09/2016InscriptoSociedad Anónima201609201639.02.372857e+095.0CABA3.0NaNNaNNaN1.01.02.0NaN2.0NaN33.0(222.964.579- 46.172.150.151](19.0, 39.0](29.0, 40.0]5
4030555414222Los Cinco Hispanos Sociedad Anónima30/09/2016InscriptoSociedad Anónima201609201627.01.879371e+075.0CABA1.0NaNNaNNaNNaN1.01.0NaN1.0NaN12.0(13.557.176- 19.975.532](19.0, 39.0](11.0, 15.0]5
4130711265356LIMEL S.A.30/09/2016InscriptoSociedad Anónima20160920169.02.020892e+065.0CABA1.0NaNNaNNaNNaN1.01.0NaN1.0NaN11.0(1.793.326- 2.483.085](8.0, 12.0](8.0, 11.0]5
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
1002530708009195PASTELINO SA28/04/2022InscriptoSociedad Anónima20220420221.03.673343e+060.0Córdoba1.0NaNNaNNaNNaN1.01.0NaN1.0NaN1.0(3.396.600- 4.727.330](0.999, 2.0](0.999, 2.0]5
1002930646700279RM RECTIFICACIONES S.A.31/08/2018InscriptoSociedad Anónima20180820181.01.269392e+063.0Corrientes1.0NaNNaNNaNNaN1.01.0NaN1.0NaN16.0(890.758- 1.302.657](0.999, 2.0](15.0, 21.0]5
1003230717686043LOZZA SAS22/08/2022InscriptoOtras Formas Societarias20220820221.05.747619e+060.0Entre Rios1.0NaNNaNNaNNaN1.01.0NaN1.0NaN40.0(4.727.330- 6.702.697](0.999, 2.0](29.0, 40.0]5
1003530711231214Encendido Bari SRL19/04/2018InscriptoS.R.L20180420181.01.179683e+063.0CABA2.0NaNNaNNaNNaN1.01.0NaN1.0NaN69.0(890.758- 1.302.657](0.999, 2.0](58.0, 97.6]5
1004830714288551AGUAS ROMANO SRL01/12/2016InscriptoS.R.L20161220161.04.215306e+055.0Entre Rios2.0NaNNaNNaNNaN1.01.0NaN1.0NaN1.0(377.939- 599.760](0.999, 2.0](0.999, 2.0]5
1006030561699867Melos Ediciones Musicales S.A.06/09/2017InscriptoSociedad Anónima20170920171.01.133333e+064.0CABA3.0NaNNaNNaN1.0NaN1.0NaN1.0NaN4.0(890.758- 1.302.657](0.999, 2.0](3.0, 4.0]5
1006133711043069VIA CARGO S.A.21/06/2019InscriptoSociedad Anónima20190620191.02.961003e+052.0Chubut1.0NaNNaNNaNNaN1.01.0NaN1.0NaN3.0(224.078- 377.939](0.999, 2.0](2.0, 3.0]5
1006430683655143SERVEC S.A.29/11/2021InscriptoSociedad Anónima20211120211.05.169498e+060.0CABA2.0NaNNaNNaNNaN1.01.0NaN1.0NaN3.0(4.727.330- 6.702.697](0.999, 2.0](2.0, 3.0]5
1006630716032503BIOPAZ S.A.11/06/2021InscriptoSociedad Anónima20210620211.07.886492e+040.0Corrientes2.0NaNNaNNaNNaN1.01.0NaN1.0NaN3.0(33.011- 104.767](0.999, 2.0](2.0, 3.0]5
1007030710308051ZENSEI SRL30/05/2017InscriptoSociedad Anónima20170520171.05.901429e+074.0CABA1.0NaNNaNNaNNaN1.01.0NaN1.0NaN1.0(46.718.747- 89.439.449](0.999, 2.0](0.999, 2.0]5